Prepare your data for import

Learn how to prepare your data for successful import.

Keep your data fresh

You must ensure your data is refreshed in the data source before a manual or scheduled connection run in order for that data to be imported for use in the intended destination or use case. For example, if you choose a daily schedule, you need to refresh your data on a daily basis, before the scheduled start time.

Make your data available

All use cases require one table or dataview per conversion event or audience list. To use the same data source for an additional conversion event or audience list, you need to create an additional table or dataview.

Some data sources require you to have proper credentials in order to make the connection, while others require that the data be accessible by Data Manager's services. See the specific guide for the data source you're using for specific guidance.

Format your data

The following sections show you how to properly format your data, to ensure it can be imported without error.

About file formats

If you are uploading a file, such as a CSV file, the first line of the file must contain the headers.

Ensure that the file has an extension, as files without extensions are rejected.

About date and time formats

Data Manager supports converting dates and times of various formats into a logical timestamp, based on six templates using three format sets: DATE, TIME, and TIMEZONE. Data Manager does not support the DATETIME type. DATETIME fields need to be converted to the STRING type in the data source, using the supported formats described in this article.

The following are examples of timestamps in supported formats:

2012-08-15T00:01:54Z (UTC ISO 8601 Standard)
2012-08-14T17:01:54-07:00 (ISO 8601 Standard with Offset)
Aug 14, 2012 17:01:54
08/14/2012T5:01:54 PM
2012-08-14 5:01:54 PM
08/14/2012 17:01:54
2012-08-14 17:01:54
08/14/2012 17:01:54*123
2012-08-14T17:01:54-07
08/14/2012T17:01:54-0700
2012-08-14T17:01:54-070000
2012-08-14T17:01:54-07:00:00
2012-08-14T17:01:54 America/Los_Angeles
Aug 14, 2012 17:01:54PST
2012-08-14 17:01:54 PST
2012-08-14 17:01:54 Pacific Standard Time
2012-08-14 17:01:54 GMT-07:00
08/14/2012 17:01:54 GMT-07:00:00

Supported date formats

Format	Example
`MMM dd, yyyy`	`Aug 14, 2012`
`MM/dd/yyyy`	`08/14/2012`
`yyyy-MM-dd`	`2012-08-14`

Supported time formats

Format	Example
`h:mm:ss a`	`5:01:54 PM`
`HH:mm:ss`	`17:01:54`
`HH:mm:ss*SSS`	`17:01:54*633` (fraction of a second)

Supported timezone formats

Description	Example
Localized Offset Text with hour (without leading zero)	`GMT-7` `GMT+5`
Localized Offset Text with 2-digit hour and minute fields, with colons	`GMT-07:00` `GMT+05:30`
Localized Offset Text with 2-digit hour, minute, and seconds fields, with colons	`GMT-07:00:00` `GMT+05:30:00`
Zero (UTC)	`Z`
Zone ID	`America/Los_Angeles` `Asia/Kolkata`
Offset hour	`-07` `+05`
Offset hour and minute with colon	`-07:00` `+05:30`
Offset hour, minute, and second with colon	`-07:00:00` `+05:30:00`
Offset hour and minute, no colon	`-0700` `+0530`
Offset hour, minute, and second, no colon	`-070000` `+053000`
Short zone name	`PST` `IST`
Long zone name	`Pacific Standard Time` `Indian Standard Time`

About hashing private customer data

To keep your data secure, private customer data that you import should be hashed. Data Manager will hash the data for you using the SHA256 algorithm, which is the industry standard for one-way hashing. The result is hex encoded. You don’t need to pre-format your data—Data Manager will normalize relevant PII fields, perform hashing and encoding for you, and push the data to the API for your use cases.

If you prefer to hash private customer data yourself, see Format your customer data file to ensure it’s formatted correctly. If you upload a hashed data file, don’t hash non-private customer data. Data Manager will push your hashed data to the API.

Note that smart hashing is automatic, meaning you will not need to select anything from the Actions menu.

Define the scope of your data import using filters

Data Manager lets you set filter conditions directly in the UI, eliminating the need to create custom data pipelines or write complicated SQL queries within your data source. When you create a filter, Data Manager only imports data from your data source (for all use cases) that satisfies all of the filter conditions.

Use filters to define audience segments for Customer Match or conversion events for Offline Conversion Import. For example, define an audience segment from your Salesforce data based on attributes such as user consent, average order value, or opportunity stage. You can apply one filter with up to 25 conditions per connection.

Create a filter

To create filters for a new data connection, add them during the Select data stage of setup:

From the Select data step of setup, click Filter to expand it.
Select the field to use to filter your data.
Select an operator.
Enter a value.
Optional: Create additional conditions, by clicking And or Or.
Continue the setup process.

To create and edit filters for an existing data connection:

From the Data manager screen, click on the connection name to edit it.
Under Filter, click Edit.
Make your changes and click Save.

Supported operators

AND
OR
Doesn’t contain (strings, integer, date, time, Boolean)
Contains (strings, integer, date, time, Boolean)
Greater than (integer)
Equal to (integer, string, date)
Less than (integer)
Before (date, time)
After (date, time)
Does not equal
Starts with
Ends with
Does not start with
Does not end with

Supported data types

Currency
Date
Time
Boolean
Integer
Dropdown (for example, a Salesforce picklist type)
String

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